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{ "cells": [ { "cell_type": "markdown", "id": "50680cbc", "metadata": {}, "source": [ "Read/load the cifar10 dataset using tf.keras.datasets\n", "- Display the first 25 images\n", "- Convert them to greyscale images by reducing the 3 colors (r,g,b) to \n", " one greyscale\n", " using the formula gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n", "- Display the first 25 images in greyscale" ] }, { "cell_type": "code", "execution_count": null, "id": "2dc5ea2f", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "id": "e846355a", "metadata": {}, "outputs": [], "source": [ "# Load the CIFAR-10 dataset\n", "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()" ] }, { "cell_type": "code", "execution_count": null, "id": "85a493d2", "metadata": {}, "outputs": [], "source": [ "# In case of special pictures\n", "selectPicture = -1 # -1 for all or number for a class starting from 0\n", "\n", "# Define a list of class names for CIFAR-10\n", "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']" ] }, { "cell_type": "code", "execution_count": null, "id": "4eacb734", "metadata": {}, "outputs": [], "source": [ "# Get the indices of images with a special label in the training set\n", "special_indices = np.where(y_train == selectPicture)[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "be2d0cd2", "metadata": {}, "outputs": [], "source": [ "# Display the first 25 images in the training set\n", "plt.figure(figsize=(10,10))\n", "for i in range(25):\n", " plt.subplot(5, 5, i+1)\n", " if selectPicture == -1 :\n", " plt.imshow(x_train[i]) # all images\n", " plt.title(class_names[y_train[i][0]])\n", " else : \n", " plt.imshow(x_train[special_indices[i]]) # special Picture only\n", " plt.title(class_names[selectPicture]) \n", " plt.xticks([])\n", " plt.yticks([])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "938e632d", "metadata": {}, "outputs": [], "source": [ "# Convert images to grayscale\n", "train_images_gray = np.dot(x_train[..., :3], [0.2989, 0.5870, 0.1140])\n", "test_images_gray = np.dot(x_test[..., :3], [0.2989, 0.5870, 0.1140])" ] }, { "cell_type": "code", "execution_count": null, "id": "96333686", "metadata": {}, "outputs": [], "source": [ "# Normalize pixel values to [0, 1]\n", "train_images_gray = train_images_gray / 255.0\n", "test_images_gray = test_images_gray / 255.0" ] }, { "cell_type": "code", "execution_count": null, "id": "64d3d2ae", "metadata": {}, "outputs": [], "source": [ "# Display the first 25 images in the training set\n", "plt.figure(figsize=(10,10))\n", "for i in range(25):\n", " plt.subplot(5, 5, i+1)\n", " if selectPicture == -1 :\n", " plt.imshow(train_images_gray[i],cmap='gray') # all images\n", " plt.title(class_names[y_train[i][0]])\n", " else : \n", " plt.imshow(train_images_gray[special_indices[i]],cmap='gray') # special Picture only\n", " plt.title(class_names[selectPicture]) \n", " plt.xticks([])\n", " plt.yticks([])\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5}
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